Search Results for author:
Found 16 papers, 7 papers with code
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models
For product search, PEFA improves the Recall@100 of the fine-tuned ERMs by an average of 5. 3% and 14. 5%, for PEFA-XS and PEFA-XL, respectively.
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering
Recent advances in few-shot question answering (QA) mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings.
printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning
Nowadays, modern recommender systems usually leverage textual and visual contents as auxiliary information to predict user preference.
Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation
Few-shot question answering (QA) aims at precisely discovering answers to a set of questions from context passages while only a few training samples are available.
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances.
InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time.
Uncertainty in Extreme Multi-label Classification
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making.
#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs
COVID-19 has caused lasting damage to almost every domain in public health, society, and economy.
Learning to Represent Human Motives for Goal-directed Web Browsing
Then we incorporate the resulting representations for enhancing the experiences of common activities people perform on the web.
Drug-Target Interaction Prediction with Graph Attention networks
For better learning and interpreting the DTI topological structure and the similarity, it is desirable to have methods specifically for predicting interactions from the graph structure.
Long Document Ranking with Query-Directed Sparse Transformer
In this paper, we design Query-Directed Sparse attention that induces IR-axiomatic structures in transformer self-attention.
``The Boating Store Had Its Best Sail Ever'': Pronunciation-attentive Contextualized Pun Recognition
In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence.
"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition
In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence.
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations.
Enhancing Air Quality Prediction with Social Media and Natural Language Processing
Accompanied by modern industrial developments, air pollution has already become a major concern for human health.
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking
An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently.
